Heart disease is the leading cause of death for men and women in the United States. Deaths from cardiovascular disease jumped globally from 12.1 million in 1990 to 20.5 million in 2021, according to a new report from the World Heart Federation. The Electrocardiogram (ECG, or EKG) is a non-invasive and efficient test that records the electrical activities of a human heart. In recent years, various approaches based on computational intelligence have been developed and successfully applied to automatic detection of cardiac arrhythmia on ECG signals. In this thesis, we study the application of Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for identification of cardiac irregularities. The two methods are tested on ECG signals with six different heartbeat conditions in the MIT- BIH Arrhythmia database. Computer simulation results show both methods are highly effective with detection rates of close to 98% and 99%, respectively.

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In the environment where new large scale problems are emerging in various disciplines and pervasive computing applications are becoming more common, there is an urgent need for machine learning algorithms that could process increasing amounts of data using comparatively smaller computing resources in a computational efficient way. Previous research has resulted in many successful learning algorithms that scale linearly or even sub-linearly with sample size and dimension, both in runtime and in space. However, linear or even sub-linear space scaling is often not sufficient, because it implies an unbounded growth in memory with sample size. This clearly opens another challenge: how to learn from large, or practically infinite, data sets or data streams using memory limited resources. Online learning is an important learning scenario in which a potentially unlimited sequence of training examples is presented one example at a time and can only be seen in a single pass. This is opposed to offline learning where the whole collection of training examples is at hand. The objective is to learn an accurate prediction model from the training stream. Upon on repetitively receiving fresh example from stream, typically, online learning algorithms attempt to update the existing model without retraining. The invention of the Support Vector Machines (SVM) attracted a lot of interest in adapting the kernel methods for both offline and online learning. Typical online learning for kernel classifiers consists of observing a stream of training examples and their inclusion as prototypes when specified conditions are met. However, such procedure could result in an unbounded growth in the number of prototypes. In addition to the danger of the exceeding the physical memory, this also implies an unlimited growth in both update and prediction time. To address this issue, in my dissertation I propose a series of kernel-based budgeted online algorithms, which have constant space and constant update and prediction time. This is achieved by maintaining a fixed number of prototypes under the memory budget. Most of the previous works on budgeted online algorithms focus on kernel perceptron. In the first part of the thesis, I review and discuss these existing algorithms and then propose a kernel perceptron algorithm which removes the prototype with the minimal impact on classification accuracy to maintain the budget. This is achieved by dual use of cached prototypes for both model presentation and validation. In the second part, I propose a family of budgeted online algorithms based on the Passive-Aggressive (PA) style. The budget maintenance is achieved by introducing an additional constraint into the original PA optimization problem. A closed-form solution was derived for the budget maintenance and model update. In the third part, I propose a budgeted online SVM algorithm. The proposed algorithm guarantees that the optimal SVM solution is maintained on all the prototype examples at any time. To maximize the accuracy, prototypes are constructed to approximate the data distribution near the decision boundary. In the fourth part, I propose a family of budgeted online algorithms for multi-class classification. The proposed algorithms are the recently proposed SVM training algorithm Pegasos. I prove that the gap between the budgeted Pegasos and the optimal SVM solution directly depends on the average model degradation due to budget maintenance. Following the analysis, I studied greedy multi-class budget maintenance methods based on removal, projection and merging of SVs. In each of these four parts, the proposed algorithms were experimentally evaluated against the state-of-art competitors. The results show that the proposed budgeted online algorithms outperform the competitive algorithm and achieve accuracy comparable to non-budget counterparts while being extremely computationally efficient. / Computer and Information Science

Yes / Face recognition is a very current subject of great interest in the area of visual computing. In the past, numerous face recognition and authentication approaches have been proposed, though the great majority of them use full frontal faces both for training machine learning algorithms and for measuring the recognition rates. In this paper, we discuss some novel experiments to test the performance of machine learning, especially the performance of deep learning, using partial faces as training and recognition cues. Thus, this study sharply differs from the common approaches of using the full face for recognition tasks. In particular, we study the rate of recognition subject to the various parts of the face such as the eyes, mouth, nose and the forehead. In this study, we use a convolutional neural network based architecture along with the pre-trained VGG-Face model to extract features for training. We then use two classifiers namely the cosine similarity and the linear support vector machine to test the recognition rates. We ran our experiments on the Brazilian FEI dataset consisting of 200 subjects. Our results show that the cheek of the face has the lowest recognition rate with 15% while the (top, bottom and right) half and the 3/4 of the face have near 100% recognition rates. / Supported in part by the European Union's Horizon 2020 Programme H2020-MSCA-RISE-2017, under the project PDE-GIR with grant number 778035.

Alcohol use disorder (AUD) remains a major public health concern globally with substantially increased mortality and a significant economic burden. The low rates of treatment and the high rates of relapse mean that excessive alcohol consumption detrimentally affects many aspects of the user's life and the lives of those around them. One reason for the low efficacy of treatments for AUD could be an unclear understanding of the neural correlates of the disease. As such, the studies in this dissertation aim at elucidating the neural mechanisms undergirding AUD, which could lead to more efficacious treatment and rehabilitation strategies. The propensity for impulsive decision making (choosing smaller, sooner rewards over larger, later ones) also known as delay discounting (DD), is an established risk-factor for a variety of substance abuse disorders, including AUD. Brain mapping of DD routinely uses modalities such as blood-oxygenation-level-dependent functional magnetic resonance imaging (BOLD fMRI). However, the extent to which these brain activation maps reflect the characteristics of impulsive behavior has not been directly studied. To examine this, we used multi-voxel pattern analysis (MVPA) methods such as multivariate classification using Support Vector Machine (SVM) algorithms and trained accurate classifiers of high vs. low impulsivity with individual fMRI brain maps. Our results demonstrate that brain regions in the prefrontal cortex encode neuroeconomic decision making characterizing DD behavior and help classify individuals with low impulsivity from individuals with high impulsivity. Individuals suffering from addictive afflictions such as AUD are often unable to plan for the future and are trapped in a narrow temporal window, resulting in short-term, impulsive decision making. Episodic future thinking (EFT) or the ability to project oneself into the future and pre-experience an event, is a rapidly growing area of addiction research and individuals suffering from addictive disorders are often poor at it. However, it has been shown across healthy individuals and disease populations (addiction, obesity) that practicing EFT reduces impulsive decision making. We provided real-time fMRI neurofeedback to alcohol users while they performed EFT inside the MR scanner to aid them in successfully modulating their thoughts between the present and the future. After the scanning session, participants made more restrained choices when performing a behavioral task outside the scanner, demonstrating an improvement in impulsivity. These two neuroimaging studies interrogate the brain mechanisms of delay discounting and episodic future thinking in alcohol use disorder. Successful classification of impulsive behavior as demonstrated in the first study could lead to accurate prediction of treatment outcomes in AUD. The second study suggests that rtfMRI provides direct access to brain mechanisms regulating EFT and highlights its potential as an intervention for impulsivity in the context of AUD. The work in this dissertation thus investigates important cognitive process for the treatment of alcohol use disorder that could pave the way for novel therapeutic interventions not only for AUD, but also for a wide spectrum of other addictive disorders. / Doctor of Philosophy / Alcohol use disorder (AUD) remains a major public health concern globally with substantially increased mortality and a significant economic burden. The low rates of treatment and the high rates of relapse mean that excessive alcohol consumption detrimentally affects many aspects of the user’s life and the lives of those around them. One reason for the low efficacy of treatments for AUD could be an unclear understanding of the brain regions affected by it. As such, the studies in this dissertation aim at elucidating the neural mechanisms undergirding AUD, which could lead to more efficacious treatment and rehabilitation strategies. The propensity for impulsive decision making (choosing smaller, sooner rewards over larger, later ones) also known as delay discounting (DD), is an established risk-factor for a variety of substance abuse disorders, including AUD. Brain mapping of DD routinely uses modalities such as blood-oxygenation-level-dependent functional magnetic resonance imaging (BOLD fMRI). However, the extent to which these brain activation maps reflect the characteristics of impulsive behavior has not been directly studied. To examine this, we searched for highly reproducible spatial patterns of brain activation that differ across experimental conditions (multi-voxel pattern analysis) and trained accurate classifiers of high vs. low impulsivity with individual fMRI brain maps. Our results demonstrate that brain regions in the prefrontal cortex encode neuroeconomic decision making and help classify individuals with low impulsivity from individuals with high impulsivity. Individuals suffering from addictive afflictions such as AUD are often unable to plan for the future and are trapped in a narrow temporal window, resulting in short-term, impulsive decision making. Episodic future thinking (EFT) or the ability to project oneself into the future and pre-experience an event, is a rapidly growing area of addiction research. However, it has been shown across healthy individuals and disease populations (addiction, obesity) that practicing EFT reduces impulsive decision making. We provided v real-time fMRI neurofeedback to alcohol users while they performed EFT inside the MR scanner to aid them in successfully modulating their thoughts between the present and the future. After the scanning session, participants made more restrained choices when performing a behavioral task outside the scanner, demonstrating an improvement in impulsivity. These two neuroimaging studies interrogate the brain mechanisms of delay discounting and episodic future thinking in alcohol use disorder. Successful classification of impulsive behavior as demonstrated in the first study could lead to accurate prediction of treatment outcomes in AUD. The second study suggests that rtfMRI provides direct access to brain mechanisms regulating EFT and highlights its potential as an intervention for impulsivity in the context of AUD. The work in this dissertation thus investigates important cognitive process for the treatment of alcohol use disorder that could pave the way for novel therapeutic interventions not only for AUD, but also for a wide spectrum of other addictive disorders.